Actionable security intelligence
Findings with remediation context and summaries for security and engineering.
AI & application security
Test code and AI systems against real-world abuse
Static analysis, OWASP LLM coverage, and model-assisted reports that engineers and leadership can both act on.
OWASP LLM Top 10Static analysisGemini explanationsAudit-ready exports
MasSecEvalSecurity evaluation summary
Overall riskMedium
Findings by severity
Crit 8High 21Med 47Low 112
Regulatory alignment72%
Policy coverage68%
Remediation progress54%
Scan scope: OWASP LLM Top 10 · API routes · Dependencies · Model-assisted review
Platform
Protection for code and AI-assisted workflows
Traceable analysis, policy, and evidence — not vague “AI magic.”
Operational security dashboard
Repo trends, severity mix, and policy posture for program owners.
Privacy & SDLC fit
Project isolation, retention controls, GitHub and pipeline ingest.
How it works
Ingest, analyze, report — one pipeline
Three stages, consistent evidence from PR check to audit pack.
01
Connect
Repos, zips, or paths — read-scoped GitHub or manual ingest.
02
Analyze
Static analysis and policies mapped to OWASP and your standards.
03
Report
Evidence for audits and concise fixes for developers.
MasSecEvalSecurity evaluation summary
Overall riskMedium
Findings by severity
Crit 8High 21Med 47Low 112
Regulatory alignment72%
Policy coverage68%
Remediation progress54%
Scan scope: OWASP LLM Top 10 · API routes · Dependencies · Model-assisted review
Security standard
OWASP Top 10 for Large Language Model Applications
Testing and reporting aligned to the industry LLM risk reference (2025).
View full OWASP documentationCritical
LLM01
Prompt Injection
Crafted inputs manipulate the model to bypass safeguards, leak instructions, or trigger unintended tool or data access.
Critical
LLM02
Sensitive Information Disclosure
Models may echo secrets, PII, or proprietary context from prompts, retrieval, or training unless outputs are validated and redacted.
High
LLM03
Supply Chain
Compromised models, datasets, plugins, or dependencies can introduce backdoors and unpredictable behaviour in production agents.
High
LLM04
Data and Model Poisoning
Adversarial or low-integrity training or fine-tuning data can skew model behaviour and embed persistent weaknesses.
High
LLM05
Improper Output Handling
Downstream components that trust LLM output without encoding, validation, or policy checks inherit injection and abuse risk.
Critical
LLM06
Excessive Agency
Over-privileged tools, autonomous loops, or broad API scopes let a single bad completion cause outsized real-world impact.
High
LLM07
System Prompt Leakage
System prompts, hidden policies, and internal instructions can be extracted and replayed to weaken defences or clone behaviour.
Medium
LLM08
Vector and Embedding Weaknesses
Poisoned chunks, weak access control on retrieval, or embedding gaps break the trust boundary between corpus and model.
Medium
LLM09
Misinformation
Confident but incorrect outputs erode trust, skew decisions, and create compliance exposure when humans over-rely on the model.
High
LLM10
Unbounded Consumption
Abuse of tokens, GPU, or paid APIs enables denial of wallet, noisy neighbour issues, and unstable cost profiles at scale.
Integrations
Works with the controls you already operate
Meet teams where they work — developers in GitHub, platform engineers in automation.
GitHub
GitHub App for scans on default branches and pull requests.
Learn more →API & automation
REST APIs for projects, scans, and reports in your pipelines.
Learn more →Controlled deployment
Self-managed components when code cannot leave your boundary.
Learn more →Why it matters
The business case for disciplined AI and application security
$4.45M+
Average breach cost (IBM, 2023)
Application and AI risk shows up as incidents, fines, and churn.
EU AI Act & NIST RMF
Emerging regulatory bar
Documentation and testing expectations for AI are tightening.
First-line defence
Engineering-led security
Early security instrumentation means fewer fire drills later.
What security leaders tell us
“Finally a platform that speaks both engineering and audit. We can show coverage, not just a slide about 'doing AI safely.'”
“OWASP alignment was the table stakes for our enterprise customers. MasSecEval made that narrative factual, not aspirational.”
“The combination of static findings and model-generated remediation notes cut our mean time to remediate dramatically.”
FAQ
Common questions
Need a deeper architecture review? Our team supports enterprise evaluations.
Talk to usHow do you handle sensitive source code?
Projects are isolated with distinct storage and vector namespaces. You control what is uploaded, and enterprise deployments can keep data inside your perimeter.
Is this only for LLMs?
No. MasSecEval's core is static analysis for conventional applications, with explicit coverage for LLM-specific risks where models and retrieval are in scope.
Can outputs feed our GRC or ticketing stack?
Yes — structured exports and APIs are designed for Jira, ServiceNow-style workflows, and executive reporting packs.
Ready to show enterprise buyers a serious security programme?
Stand up a workspace, run your first evaluation, or walk through a tailored demo.